Instructions to use vikaskapur/sentimental with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vikaskapur/sentimental with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vikaskapur/sentimental")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vikaskapur/sentimental") model = AutoModelForSequenceClassification.from_pretrained("vikaskapur/sentimental", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from vikaskapur/sentimental: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/vikaskapur/sentimental/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://vikaskapur/sentimental@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/vikaskapur/sentimental/resolve/refs%2Fpr%2F1/pytorch_model.bin
438 MB
- Xet hash:
- e943e5226f72250e33528d4bce288f1185a98792f8a7ff31605f4c2668327f71
- Size of remote file:
- 438 MB
- SHA256:
- 7891de1c3ae74e265583efeeb0a8b867fbdbf8d64a0c2f9aa6a81ac2cd2a87d7
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